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Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources

Author

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  • Junpeng Zhu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Ping Jiang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Wei Gu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Wanxing Sheng

    (China Electric Power Research Institute, Beijing 100192, China)

  • Xiaoli Meng

    (China Electric Power Research Institute, Beijing 100192, China)

  • Jun Gao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

The coming interaction between a growing electrified vehicle fleet and the desired growth in renewable energy provides new insights into the economic dispatch (ED) problem. This paper presents an economic dispatch model that considers electric vehicle charging, battery exchange stations, and wind farms. This ED model is a high-dimensional, non-linear, and stochastic problem and its solution requires powerful methods. A new finite action-set learning automata (FALA)-based approach that has the ability to adapt to a stochastic environment is proposed. The feasibility of the proposed approach is demonstrated in a modified IEEE 30 bus system. It is compared with continuous action-set learning automata and particle swarm optimization-based approaches in terms of convergence characteristics, computational efficiency, and solution quality. Simulation results show that the proposed FALA-based approach was indeed capable of more efficiently obtaining the approximately optimal solution. In addition, by using an optimal dispatch schedule for the interaction between electric vehicle stations and power systems, it is possible to reduce the gap between demand and power generation at different times of the day.

Suggested Citation

  • Junpeng Zhu & Ping Jiang & Wei Gu & Wanxing Sheng & Xiaoli Meng & Jun Gao, 2014. "Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources," Energies, MDPI, vol. 7(7), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:7:p:4629-4647:d:38417
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    References listed on IDEAS

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    Cited by:

    1. Stefano Rinaldi & Marco Pasetti & Emiliano Sisinni & Federico Bonafini & Paolo Ferrari & Mattia Rizzi & Alessandra Flammini, 2018. "On the Mobile Communication Requirements for the Demand-Side Management of Electric Vehicles," Energies, MDPI, vol. 11(5), pages 1-27, May.
    2. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.

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